Looking for Sustainable Urban Mobility through Bayesian Networks

There is no formalised theory of sustainable urban mobility systems. Observed patterns of urban mobility are often considered unsustainable. But we don’t know what a city with sustainable mobility should look like. It is nevertheless increasingly apparent that the urban mobility system plays an impo...

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Autor principal:	Giovanni Fusco
Formato:	article
Lenguaje:	DE EN FR IT PT
Publicado:	Unité Mixte de Recherche 8504 Géographie-cités 2004
Materias:	system urban sustainability urban mobility bayesian networks bayesian inference world city Geography (General) G1-922
Acceso en línea:	https://doaj.org/article/b7e5bce4a3824f19a673cb02ea477a4d
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id	oai:doaj.org-article:b7e5bce4a3824f19a673cb02ea477a4d
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spelling	oai:doaj.org-article:b7e5bce4a3824f19a673cb02ea477a4d2021-12-02T11:12:55ZLooking for Sustainable Urban Mobility through Bayesian Networks1278-336610.4000/cybergeo.2777https://doaj.org/article/b7e5bce4a3824f19a673cb02ea477a4d2004-11-01T00:00:00Zhttp://journals.openedition.org/cybergeo/2777https://doaj.org/toc/1278-3366There is no formalised theory of sustainable urban mobility systems. Observed patterns of urban mobility are often considered unsustainable. But we don’t know what a city with sustainable mobility should look like. It is nevertheless increasingly apparent that the urban mobility system plays an important role in the achievement of the city’s wider sustainability objectives.In this paper we explore the characteristics of sustainable urban mobility systems through the technique of Bayesian networks. At the frontier between multivariate statistics and artificial intelligence, Bayesian networks provide powerful models of causal knowledge in an uncertain context. Using data on urban structure, transportation offer, mobility demand, resource consumption and environmental externalities from seventy-five world cities, we developed a systemic model of the city-transportation-environment interaction in the form of a Bayesian network. The network could then be used to infer the features of the city with sustainable mobility.The Bayesian model indicates that the city with sustainable mobility is most probably a dense city with highly efficient transit and multimodal mobility. It produces high levels of accessibility without relying on a fast road network. The achievement of sustainability objectives for urban mobility is probably compatible with all socioeconomic contexts.By measuring the distance of world cities from the inferred sustainability profile, we finally derive a geography of sustainability for mobility systems. The cities closest to the sustainability profile are in Central Europe as well as in affluent countries of the Far East. Car-dependent American cities are the farthest from the desired sustainability profile.Giovanni FuscoUnité Mixte de Recherche 8504 Géographie-citésarticlesystemurban sustainabilityurban mobilitybayesian networksbayesian inferenceworld cityGeography (General)G1-922DEENFRITPTCybergeo (2004)
institution	DOAJ
collection	DOAJ
language	DE EN FR IT PT
topic	system urban sustainability urban mobility bayesian networks bayesian inference world city Geography (General) G1-922
spellingShingle	system urban sustainability urban mobility bayesian networks bayesian inference world city Geography (General) G1-922 Giovanni Fusco Looking for Sustainable Urban Mobility through Bayesian Networks
description	There is no formalised theory of sustainable urban mobility systems. Observed patterns of urban mobility are often considered unsustainable. But we don’t know what a city with sustainable mobility should look like. It is nevertheless increasingly apparent that the urban mobility system plays an important role in the achievement of the city’s wider sustainability objectives.In this paper we explore the characteristics of sustainable urban mobility systems through the technique of Bayesian networks. At the frontier between multivariate statistics and artificial intelligence, Bayesian networks provide powerful models of causal knowledge in an uncertain context. Using data on urban structure, transportation offer, mobility demand, resource consumption and environmental externalities from seventy-five world cities, we developed a systemic model of the city-transportation-environment interaction in the form of a Bayesian network. The network could then be used to infer the features of the city with sustainable mobility.The Bayesian model indicates that the city with sustainable mobility is most probably a dense city with highly efficient transit and multimodal mobility. It produces high levels of accessibility without relying on a fast road network. The achievement of sustainability objectives for urban mobility is probably compatible with all socioeconomic contexts.By measuring the distance of world cities from the inferred sustainability profile, we finally derive a geography of sustainability for mobility systems. The cities closest to the sustainability profile are in Central Europe as well as in affluent countries of the Far East. Car-dependent American cities are the farthest from the desired sustainability profile.
format	article
author	Giovanni Fusco
author_facet	Giovanni Fusco
author_sort	Giovanni Fusco
title	Looking for Sustainable Urban Mobility through Bayesian Networks
title_short	Looking for Sustainable Urban Mobility through Bayesian Networks
title_full	Looking for Sustainable Urban Mobility through Bayesian Networks
title_fullStr	Looking for Sustainable Urban Mobility through Bayesian Networks
title_full_unstemmed	Looking for Sustainable Urban Mobility through Bayesian Networks
title_sort	looking for sustainable urban mobility through bayesian networks
publisher	Unité Mixte de Recherche 8504 Géographie-cités
publishDate	2004
url	https://doaj.org/article/b7e5bce4a3824f19a673cb02ea477a4d
work_keys_str_mv	AT giovannifusco lookingforsustainableurbanmobilitythroughbayesiannetworks
_version_	1718396131247915008

Looking for Sustainable Urban Mobility through Bayesian Networks

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